BibTex RIS Kaynak Göster

Moving Object Detection Using an Adaptive Background Modeling in Dynamic Scene

Yıl 2017, Cilt: 2 Sayı: 1, 161 - 166, 25.02.2017

Öz

Determination of moving foreground objects in dynamic scenes
for video surveillance systems is still a problem can not be resolved exactly.
In the literature; pixel-based, block-based and texture-based methods have been
proposed  to solve this problem. The
method we propose will be block-based method which can be applied to real time
in dynamic scenes. We have created non-overlapped  blocks with the averages the pixels in the
gray level. We used this average value to generate the background model based
on a modified original KDE (Kernel Density Estimation) method. To determine the
moving foreground objects and  to update
background model, we use an adaptive parameter which is determined  according to 
the number of changes in the state of this pixel during the last N
frames. Performance evaluation of the proposed method is tested by background
methods in literature without applying post-processing techniques. Experimental
results demonstrate the effectiveness and robustness of our method.

Kaynakça

  • [1]. C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, “ Pfinder: Real-time tracking of the human body,” IEEE Trans. Pattern Anal. 1997, vol.19, pp.780–785.
  • [2]. N. Friedman and S. Russell, “Image segmentation in video sequences: a probabilistic approach,” In Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., 1997, pp. 175–181.
  • C.Stauffer and W.E.L, Grimson, “Adaptive background mixture models for real-time tracking,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, CA, USA, June 1998, pp. 246–252.
  • P,KaewTraKulPong and R,Bowden, “ An improved adaptive background mixture model for real-time tracking with shadow detection,” In Proceedings of 2nd European Workshop on Advanced Video-Based Surveillance Systems(AVBS01), Sept. 2001.
  • R.Yan, X.Song and S.Yan, “Moving Object Detection Based on an Improved Gaussian Mixture Background Model”, Computing, Communication, Control, and Management, 2009. CCCM 2009. ISECS International Colloquium on, pp. 12–15.
  • K.Kim, T.H,Chalidabhongse, D.Harwood and L.Davis, “Real-time foreground-background segmentation using codebook model,” Real-Time Imaging, vol.11, pp.172–185, 2005
  • Q.Tu, Y. Xu and M.Zhou, “Box-based Codebook Model for Real-time Objects Detection,” Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on, 2008, pp. 7621-7625.
  • L.Maddalena and A.Petrosino,“A self-organizing approach to background subtraction for visual surveillance applications,” IEEE Trans. Image Process 2008, pp. 1168–1177.
  • A.Elgammal, R.Duraiswami, D. Harwood and L.S.Davis, “Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,” Proc. IEEE 2002, vol.90, pp.1151–1163.
  • C.Ianasi, V. Gui, C. Toma and D.Pescaru, “ A fast algorithm for background tracking in video surveillance, using nonparametric kernel density estimation,” Facta Universitatis (NIS) Ser.: Elec. Energ. , vol.18, pp.127–144,2005
  • T.Tanaka, A.Shimada, D. Arita and R.I Taniguchi, “A Fast Algorithm for Adaptive Background Model Construction Using Parzen Density Estimation”, Advanced Video and Signal Based Surveillance, 2007. AVSS 2007. IEEE Conference on London. 2007, pp.528 – 533
  • J.G.Park and C.Lee, “Bayesian rule-based complex background modeling and foreground detection, “Optical Engineering, vol.49 No.2, 2010.
  • M. Hofmann, P. Tiefenbacher, G. Rigoll, “Background segmentation with feedback: The pixel-based adaptive segmenter,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW),2012, pp. 38–43.
  • J.Yao and J.M.Odobez, “Multi-Layer Background Subtraction Based on Color and Texture”, IEEE Conference on Computer Vision and Pattern Recognition, 2007. ,pp. 1–8.
  • J.Lee and M.Park, “An Adaptive Background Subtraction Method Based on Kernel Density Estimation,” Sensors , vol.12, pp.12279-12300, 2012.
  • M.Casares, S.Velipasalar and A.Pinto, “Light-weight salient foreground detection for embedded smart cameras,” Computer Vision and Image Understanding, pp. 1223–1237, 2010.
  • Wallflower dataset web site.[Online].Available: http://research.microsoft.com/en-us/um/people/jckrumm/WallFlower/ TestImages.htm
  • Li dataset web site.[Online]. Available: http://perception.i2r.a-star.edu.sg/bk_model/bk_index.html
  • Andrews Sobral web site.[Online]. Available: https://github.com/andrewssobral/bgslibrary
  • S.Brutzer, B.Hoferlin and G.Heidemann, “Evaluation of background subtraction techniques for video surveillance,” in: Proceedings of the CVPR, IEEE, 2011, pp. 1937–1944.
  • EC Funded CAVIAR project/IST 2001 37540, web site.[Online]. Available: http://homepages.inf.ed.ac.uk/rbf/ CAVIAR/
  • Fida El Baf, T.Bouwmans, B.Vachon, “Type-2 Fuzzy Mixture of Gaussians Model: Application to Background Modeling,” Advances in Visual Computing, vol. 5358, pp. 772-78,2008.
  • Demirel. H, Sayısal Elektronik, 2nd ed., İstanbul, Birsen Yayınevi, 2015.
  • Topaloğlu. N, Mikroişlemciler ve Assembly Dili, 6nd ed., Seçkin Yayıncılık, Ankara, 2015.
  • S.Savaş, N.Topaloğlu, B. Ciylan, “Analysis of mobile communication signals with frequency analysis method”. Gazi University Journal of Science, Vol.25,No.2 , pp.447-454,2012.
Yıl 2017, Cilt: 2 Sayı: 1, 161 - 166, 25.02.2017

Öz

Kaynakça

  • [1]. C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, “ Pfinder: Real-time tracking of the human body,” IEEE Trans. Pattern Anal. 1997, vol.19, pp.780–785.
  • [2]. N. Friedman and S. Russell, “Image segmentation in video sequences: a probabilistic approach,” In Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers Inc., 1997, pp. 175–181.
  • C.Stauffer and W.E.L, Grimson, “Adaptive background mixture models for real-time tracking,” In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, CA, USA, June 1998, pp. 246–252.
  • P,KaewTraKulPong and R,Bowden, “ An improved adaptive background mixture model for real-time tracking with shadow detection,” In Proceedings of 2nd European Workshop on Advanced Video-Based Surveillance Systems(AVBS01), Sept. 2001.
  • R.Yan, X.Song and S.Yan, “Moving Object Detection Based on an Improved Gaussian Mixture Background Model”, Computing, Communication, Control, and Management, 2009. CCCM 2009. ISECS International Colloquium on, pp. 12–15.
  • K.Kim, T.H,Chalidabhongse, D.Harwood and L.Davis, “Real-time foreground-background segmentation using codebook model,” Real-Time Imaging, vol.11, pp.172–185, 2005
  • Q.Tu, Y. Xu and M.Zhou, “Box-based Codebook Model for Real-time Objects Detection,” Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on, 2008, pp. 7621-7625.
  • L.Maddalena and A.Petrosino,“A self-organizing approach to background subtraction for visual surveillance applications,” IEEE Trans. Image Process 2008, pp. 1168–1177.
  • A.Elgammal, R.Duraiswami, D. Harwood and L.S.Davis, “Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,” Proc. IEEE 2002, vol.90, pp.1151–1163.
  • C.Ianasi, V. Gui, C. Toma and D.Pescaru, “ A fast algorithm for background tracking in video surveillance, using nonparametric kernel density estimation,” Facta Universitatis (NIS) Ser.: Elec. Energ. , vol.18, pp.127–144,2005
  • T.Tanaka, A.Shimada, D. Arita and R.I Taniguchi, “A Fast Algorithm for Adaptive Background Model Construction Using Parzen Density Estimation”, Advanced Video and Signal Based Surveillance, 2007. AVSS 2007. IEEE Conference on London. 2007, pp.528 – 533
  • J.G.Park and C.Lee, “Bayesian rule-based complex background modeling and foreground detection, “Optical Engineering, vol.49 No.2, 2010.
  • M. Hofmann, P. Tiefenbacher, G. Rigoll, “Background segmentation with feedback: The pixel-based adaptive segmenter,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW),2012, pp. 38–43.
  • J.Yao and J.M.Odobez, “Multi-Layer Background Subtraction Based on Color and Texture”, IEEE Conference on Computer Vision and Pattern Recognition, 2007. ,pp. 1–8.
  • J.Lee and M.Park, “An Adaptive Background Subtraction Method Based on Kernel Density Estimation,” Sensors , vol.12, pp.12279-12300, 2012.
  • M.Casares, S.Velipasalar and A.Pinto, “Light-weight salient foreground detection for embedded smart cameras,” Computer Vision and Image Understanding, pp. 1223–1237, 2010.
  • Wallflower dataset web site.[Online].Available: http://research.microsoft.com/en-us/um/people/jckrumm/WallFlower/ TestImages.htm
  • Li dataset web site.[Online]. Available: http://perception.i2r.a-star.edu.sg/bk_model/bk_index.html
  • Andrews Sobral web site.[Online]. Available: https://github.com/andrewssobral/bgslibrary
  • S.Brutzer, B.Hoferlin and G.Heidemann, “Evaluation of background subtraction techniques for video surveillance,” in: Proceedings of the CVPR, IEEE, 2011, pp. 1937–1944.
  • EC Funded CAVIAR project/IST 2001 37540, web site.[Online]. Available: http://homepages.inf.ed.ac.uk/rbf/ CAVIAR/
  • Fida El Baf, T.Bouwmans, B.Vachon, “Type-2 Fuzzy Mixture of Gaussians Model: Application to Background Modeling,” Advances in Visual Computing, vol. 5358, pp. 772-78,2008.
  • Demirel. H, Sayısal Elektronik, 2nd ed., İstanbul, Birsen Yayınevi, 2015.
  • Topaloğlu. N, Mikroişlemciler ve Assembly Dili, 6nd ed., Seçkin Yayıncılık, Ankara, 2015.
  • S.Savaş, N.Topaloğlu, B. Ciylan, “Analysis of mobile communication signals with frequency analysis method”. Gazi University Journal of Science, Vol.25,No.2 , pp.447-454,2012.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Makaleler
Yazarlar

M.fatih Savas

Yayımlanma Tarihi 25 Şubat 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 2 Sayı: 1

Kaynak Göster

APA Savas, M. (2017). Moving Object Detection Using an Adaptive Background Modeling in Dynamic Scene. European Journal of Engineering and Natural Sciences, 2(1), 161-166.
AMA Savas M. Moving Object Detection Using an Adaptive Background Modeling in Dynamic Scene. European Journal of Engineering and Natural Sciences. Şubat 2017;2(1):161-166.
Chicago Savas, M.fatih. “Moving Object Detection Using an Adaptive Background Modeling in Dynamic Scene”. European Journal of Engineering and Natural Sciences 2, sy. 1 (Şubat 2017): 161-66.
EndNote Savas M (01 Şubat 2017) Moving Object Detection Using an Adaptive Background Modeling in Dynamic Scene. European Journal of Engineering and Natural Sciences 2 1 161–166.
IEEE M. Savas, “Moving Object Detection Using an Adaptive Background Modeling in Dynamic Scene”, European Journal of Engineering and Natural Sciences, c. 2, sy. 1, ss. 161–166, 2017.
ISNAD Savas, M.fatih. “Moving Object Detection Using an Adaptive Background Modeling in Dynamic Scene”. European Journal of Engineering and Natural Sciences 2/1 (Şubat 2017), 161-166.
JAMA Savas M. Moving Object Detection Using an Adaptive Background Modeling in Dynamic Scene. European Journal of Engineering and Natural Sciences. 2017;2:161–166.
MLA Savas, M.fatih. “Moving Object Detection Using an Adaptive Background Modeling in Dynamic Scene”. European Journal of Engineering and Natural Sciences, c. 2, sy. 1, 2017, ss. 161-6.
Vancouver Savas M. Moving Object Detection Using an Adaptive Background Modeling in Dynamic Scene. European Journal of Engineering and Natural Sciences. 2017;2(1):161-6.